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Slack extender mechanism for greening dependent-tasks scheduling on DVFS-enabled computing platforms

Abstract

The task’s slack is the key issue to reduce the energy consumed by DVFS-enabled computing platforms. Despite the large number of scheduling algorithms that are presented in the literature, only a unique scaling axiomatic approach (SAA) is utilized in the scaling phase of the algorithms. SAA simply extends the execution of the task within its slack if a suitable scaling frequency is available. Unfortunately, when dependent-tasks applications are scheduled on such platforms, scheduling algorithms minimize the tasks’ slacks to reduce the overall completion time of the application tasks. This paper presents a mechanism that can be applied to any schedule produced by a dependent-task scheduling algorithm for both homogeneous and heterogeneous DVFS-enabled computing platforms. The proposed mechanism is called BlackLight. BlackLight attempts to extend the tasks’ slacks via rescheduling the application tasks without violating the overall completion time of the application tasks. The proposed mechanism is applied to a large number of dependent-tasks schedules of both random generated application graphs and two real-world application graphs. The experimental results based on a computer simulation show that the proposed mechanism significantly extends the tasks’ slacks compared with SAA , which leads to more reduction in the consumed energy.

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Correspondence to Tarek Hagras.

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Hagras, T. Slack extender mechanism for greening dependent-tasks scheduling on DVFS-enabled computing platforms. J Supercomput (2021). https://doi.org/10.1007/s11227-021-04112-9

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Keywords

  • Dynamic voltage/frequency scaling
  • High-performance computing
  • Dependent-task scheduling
  • Greening computing
  • Energy aware task scheduling